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Hands-On Machine Learning for Cybersecurity

You're reading from   Hands-On Machine Learning for Cybersecurity Safeguard your system by making your machines intelligent using the Python ecosystem

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781788992282
Length 318 pages
Edition 1st Edition
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Authors (2):
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Soma Halder Soma Halder
Author Profile Icon Soma Halder
Soma Halder
Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
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Table of Contents (13) Chapters Close

Preface 1. Basics of Machine Learning in Cybersecurity FREE CHAPTER 2. Time Series Analysis and Ensemble Modeling 3. Segregating Legitimate and Lousy URLs 4. Knocking Down CAPTCHAs 5. Using Data Science to Catch Email Fraud and Spam 6. Efficient Network Anomaly Detection Using k-means 7. Decision Tree and Context-Based Malicious Event Detection 8. Catching Impersonators and Hackers Red Handed 9. Changing the Game with TensorFlow 10. Financial Fraud and How Deep Learning Can Mitigate It 11. Case Studies 12. Other Books You May Enjoy

Segregating Legitimate and Lousy URLs

A recent study showed that 47% of the world's population is online right now. With the World Wide Web (WWW) at our disposal, we find ourselves fiddling with the various internet sites on offer. However, this exposes us to the most dangerous threat of all, because we are not able distinguish between a legitimate URL and a malicious URL.

In this chapter, we will use a machine learning approach to easily tell the difference between benign and malicious URLs. This chapter will cover the following topics:

  • Understanding URLs and how they fit in the internet address scheme
  • Introducing malicious URLs
  • Looking at the different ways malicious URLs propagate
  • Using heuristics to detect malicious URLs
  • Using machine learning to detect malicious URLs

A URL stands for uniform resource locator. A URL is essentially the address of a web page located in...

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